






import numpy

# List of input, output pairs
UpperCAmelCase__      			=						(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
UpperCAmelCase__      			=						(((515, 22, 13), 555), ((61, 35, 49), 150))
UpperCAmelCase__      			=						[2, 4, 1, 5]
UpperCAmelCase__      			=						len(train_data)
UpperCAmelCase__      			=						0.009







def     a_   (__A				,       __A="train"      )				->						Optional[int]:



					"""simple docstring"""
					return calculate_hypothesis_value(_A				,       _A      ) - output(
					    _A				,       _A      )







def     a_   (__A      )				->						Any:



					"""simple docstring"""
					__a					:						List[str]      =					0
					for i in range(len(_A      ) - 1      ):
										hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
					hyp_val += parameter_vector[0]
					return hyp_val







def     a_   (__A				,       __A      )				->						Any:



					"""simple docstring"""
					if data_set == "train":
										return train_data[example_no][1]
					elif data_set == "test":
										return test_data[example_no][1]
					return None







def     a_   (__A				,       __A      )				->						Optional[int]:



					"""simple docstring"""
					if data_set == "train":
										return _hypothesis_value(train_data[example_no][0]      )
					elif data_set == "test":
										return _hypothesis_value(test_data[example_no][0]      )
					return None







def     a_   (__A				,       __A=m      )				->						Optional[Any]:



					"""simple docstring"""
					__a					:						List[str]      =					0
					for i in range(_A      ):
										if index == -1:
															summation_value += _error(_A      )
										else:
															summation_value += _error(_A      ) * train_data[i][0][index]
					return summation_value







def     a_   (__A      )				->						Any:



					"""simple docstring"""
					__a					:						List[Any]      =					summation_of_cost_derivative(_A				,       _A      ) / m
					return cost_derivative_value







def     a_   ()				->						Dict:



					"""simple docstring"""
					global parameter_vector
					# Tune these values to set a tolerance value for predicted output
					__a					:						Optional[int]      =					0.00_0002
					__a					:						List[Any]      =					0
					__a					:						int      =					0
					while True:
										j += 1
										__a					:						str      =					[0, 0, 0, 0]
										for i in range(0				,       len(_A      )      ):
															__a					:						Dict      =					get_cost_derivative(i - 1      )
															__a					:						Optional[Any]      =					(
															    parameter_vector[i] - LEARNING_RATE * cost_derivative
															)
										if numpy.allclose(
										    _A				,       _A				,       atol=_A				,       rtol=_A				,       ):
															break
										__a					:						Dict      =					temp_parameter_vector
					print(("Number of iterations:", j)      )







def     a_   ()				->						List[Any]:



					"""simple docstring"""
					for i in range(len(_A      )      ):
										print(("Actual output value:", output(_A				,       "test"      ))      )
										print(("Hypothesis output:", calculate_hypothesis_value(_A				,       "test"      ))      )


if __name__ == "__main__":
			run_gradient_descent()
			print('''\nTesting gradient descent for a linear hypothesis function.\n''')
			test_gradient_descent()










'''simple docstring'''






from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class 				a__(   lowerCamelCase__      ):
    lowercase__					=       ["""image_processor""", """tokenizer"""]
    lowercase__					=       """BridgeTowerImageProcessor"""
    lowercase__					=       ("""RobertaTokenizer""", """RobertaTokenizerFast""")







    def __init__( self					:    int		,     __snake_case					:    str		,     __snake_case					:    List[str]      ):
      super().__init__(__snake_case		,     __snake_case      )







    def __call__( self					:    int		,     __snake_case					:    Optional[Any]		,     __snake_case					:    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None		,     __snake_case					:    bool = True		,     __snake_case					:    Union[bool, str, PaddingStrategy] = False		,     __snake_case					:    Union[bool, str, TruncationStrategy] = None		,     __snake_case					:    Optional[int] = None		,     __snake_case					:    int = 0		,     __snake_case					:    Optional[int] = None		,     __snake_case					:    Optional[bool] = None		,     __snake_case					:    Optional[bool] = None		,     __snake_case					:    bool = False		,     __snake_case					:    bool = False		,     __snake_case					:    bool = False		,     __snake_case					:    bool = False		,     __snake_case					:    bool = True		,     __snake_case					:    Optional[Union[str, TensorType]] = None		,     **__snake_case					:    List[Any]		,     ):
      a		:   Optional[int]	  =	self.tokenizer(
          text=__snake_case		,     add_special_tokens=__snake_case		,     padding=__snake_case		,     truncation=__snake_case		,     max_length=__snake_case		,     stride=__snake_case		,     pad_to_multiple_of=__snake_case		,     return_token_type_ids=__snake_case		,     return_attention_mask=__snake_case		,     return_overflowing_tokens=__snake_case		,     return_special_tokens_mask=__snake_case		,     return_offsets_mapping=__snake_case		,     return_length=__snake_case		,     verbose=__snake_case		,     return_tensors=__snake_case		,     **__snake_case		,     )
      # add pixel_values + pixel_mask
      a		:   List[str]	  =	self.image_processor(
          __snake_case		,     return_tensors=__snake_case		,     do_normalize=__snake_case		,     do_center_crop=__snake_case		,     **__snake_case      )
      encoding.update(__snake_case      )

      return encoding







    def      lowercase_			( self					:    int		,     *__snake_case					:    List[str]		,     **__snake_case					:    List[str]      ):
      return self.tokenizer.batch_decode(*__snake_case		,     **__snake_case      )







    def      lowercase_			( self					:    List[str]		,     *__snake_case					:    Tuple		,     **__snake_case					:    Union[str, Any]      ):
      return self.tokenizer.decode(*__snake_case		,     **__snake_case      )







    @property
    def      lowercase_			( self					:    Optional[Any]      ):
      a		:   Optional[Any]	  =	self.tokenizer.model_input_names
      a		:   Union[str, Any]	  =	self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )